In Gaussian processes the main challenge is the definition of valid kernels (covariance functions) able to capture the relationships between different tasks. This paper presents a novel methodology to construct valid multi-task covariance functions (Mercer kernels) for Gaussian processes allowing for a ...
Multi-kernel gaussian processes. Proceed- ings of the Twenty-Second International Joint Conference on Artificial Intelli- gence, pages 1408-1413, 2011.A. Melkumyan and F. Ramos, "Multi-kernel gaussian processes," in Proceedings of the Twenty-Second international joint conference on Artificial ...
Joe Qin.Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis. Industrial and Engineering Chemistry . 2007... Yingwei,Zhang,S.,... - 《Industrial & Engineering Chemistry Research》 被引量: 95发表: 2007年 Nonlinear and Non-Gaussian Dynamic Batch Process Monitoring...
As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian ...
Kernel methods, such as Support Vector Machines or Gaussian Processes, offer characteristics such as a better generalization ability or the availability of uncertainty estimations, that may make them more suitable for small to medium size datasets. As a consequence, kernelbased MTL methods stand out ...
Considering the non-linear and unstable problem in the bearing signal31, the multi-kernel function (as in Eq. (5)), defined as the convex combination ofdGaussian kernels (the RBF kernel), is introduced to evaluate those problems. Theoretically, larger bandwidth values of the RBF kernel can ma...
As a special case of ridge regression, correlation filters generate their training set {xi|i = 0, . . . , l−1} by cyclically shifting a base sample, x ∈ Rl, such that xi = Pilx, where Pl is the permutation matrix of l × l [26], and the yis are often Gaussian labels. ...
Gaussian processesimage segmentationmedical image processingpattern clusteringspeckletumoursAutomated segmentation of tumors in breast ultrasound (US) images is challenging due to poor image quality, presence of speckle noise, shadowing effects and acoustic enhancement. This paper improves the multi-scale ...
[19] used the ELM model and Gaussian Process Regression (GPR) to predict water level. They used the historical datasets at four previous time steps to predict water levels. The ELM was able to successfully water level. Seidu et al. [20] coupled wavelet transform-self adaptive differential ...
Three kernel functions were used: the linear kernel, the polynomial kernel, and the Gaussian kernel, with weights [26] of 𝑤1w1:𝑤2w2:𝑤3w3 (𝑤1∈[0.1, 1], 𝑤2∈[0.1, 1], 𝑤3∈[0.1, 1]w1∈[0.1, 1], w2∈[0.1, 1], w3∈[0.1, 1]), as shown in Formula (7)....